Maybe you're right, but I think there's something to be said for teaching applied ML separate from the theory. I took a very in-depth theory-focused ML class at Caltech, in which lectures made up a thoroughly rigorous mathematical introduction, and the problem sets were only maybe 10% applications. I and my classmates came out of the class feeling super comfortable with the theory behind learning in general and how it applied to many of the standard ML algorithms, but without any experience actually working with very large data sets and building ML algorithm implementations that scale. That's what I'm hoping this book will help with, and I think it is appropriate to separate that kind of material from the theory.

Good point. Does one really need to know how to code up a neural network in order to just use it? This has been my biggest frustration with some teachers of Machine Learning. They need to abstract away the innards and provide a usable high-level interface. I know this is hard to do. ML isn't magic ... you have to know what you are doing. But the same can be said for the automobile when it was first invented. Today, to effectively use a car, you don't need to know anything about engines.

I think these books are not really meant to be a PhD education but more of a tutorial introduction to the field. What O'Reilly is doing right now is really important. As Steve Yegge said, they are "trying to provoke a culture change."

Would-be brogrammers will find these books, read them, and graduate from cat-picture projects into more sophisticated applications that can solve real computer science problems. It matters less whether the readers of these books actually get a comprehensive understanding of the theoretical knowledge behind them, then it does that they enrich readers and spread the desire to learn and attain true understanding of the field.

Steve Yegge reference: http://www.youtube.com/watch?v=vKmQW_Nkfk8

You underestimate the value of ML for monetizing cat pictures.

Simple machine learning isn't particularly magical and many of the simpler techniques are pretty trivial and can give perfectly usable results without needing much of a thorough theoretical background. Sure you can get better results faster if you dig into the details and really learn and understand what you are doing, but many real world problems don't really require that. No need to wrap up simple usable algorithms and shrouds of high-brow mysticism.

I agree you often don't need to know some of the guts of the algorithms, but what they're doing and how to interpret their results is important imo. There is a lot of misuse of statistics, or at least sloppy use of statistics, in the "applied data mining" field. In particular, when interpreting the results you usually need to be careful with paying attention to what assumptions went into generating them, or else it's easy to make stronger claims than the data really warrants.

And here I totally agree. I guess the message that needs to be made clear is that doing machine learning is really quite easy and really nothing special, doing something meaningful with the numbers that come out from your program is the hard part that should be focused on.